Detecting and Fixing Unfairness in Data-Driven
                                Decision Making
                                H. V. Jagadish1
                                1
                                    University of Michigan, U.S.A.


                                               Abstract
                                               On 26th June 2024, H. V. Jagadish delivered a keynote talk at the 32nd Symposium on Advanced Database
                                               Systems in Villasimius (Sardinia, Italy). The following is the abstract of his talk and a short biography




                                Abstract of the Keynote
                                Algorithmic decision-makers can be unfairly biased for many reasons. Most of these have to do
                                with the data. We consider challenges in the data used as well as in the results produced. We
                                will look at multiple examples of how to detect unfairness and how to mitigate it when found.


                                Short Biography
                                H. V. Jagadish is Edgar F. Codd Distinguished University Professor and Bernard A Galler
                                Collegiate Professor of Electrical Engineering and Computer Science at the University of
                                Michigan in Ann Arbor, and Director of the Michigan Institute for Data Science. Prior to
                                1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.
                                Professor Jagadish is well known for his broad-ranging research on information management,
                                and has over 200 major papers and 38 patents, with an H-index of 101. He is a fellow of the
                                ACM since 2003 and of AAAS since 2018. He currently chairs the board of the Academic Data
                                Science Alliance and previously served on the board of the Computing Research Association
                                (2009-2018). He has been an Associate Editor for the ACM Transactions on Database Systems
                                (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of
                                the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009),
                                Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program
                                Chair of the VLDB Conference (2014). Since 2016, he is Editor of the Springer (previously
                                Morgan & Claypool) Synthesis Lecture Series on Data Management. Among his many awards,
                                he won the David E Liddle Research Excellence Award (at the University of Michigan) in 2008,
                                the ACM SIGMOD Contributions Award in 2013, and the Distinguished Faculty Achievement
                                Award (at the University of Michigan) in 2019. His popular MOOC on Data Science Ethics is
                                available on both EdX and Coursera.

                                SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
                                Envelope-Open jag@umich.edu (H. V. Jagadish)
                                Orcid 0000-0003-0724-5214 (H. V. Jagadish)
                                             © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).




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